This paper tackles two challenges to discovery of graph rules. Existing discovery methods
often (a) return an excessive number of rules, and (b) do not scale with large graphs given …

Unique column combinations (UCCs) are a fundamental concept in relational databases.
They identify entities in the data and support various data management activities. Still, UCCs …

Data cleaning is considered as an effective approach of improving data quality in order to
help practitioners and researchers be devoted to downstream analysis and decision-making …

Rule discovery from large datasets is often prohibitively costly. The problem becomes more
staggering when the rules are collectively defined across multiple tables. To scale with large …

This paper studies two questions about rule discovery. Can we characterize the usefulness
of rules using quantitative criteria? How can we discover rules using those criteria? As a …

We investigate the problem of discovering approximate denial constraints (DCs), for finding
DCs that hold with some exceptions to avoid overfitting real-life dirty data and facilitate data …

Denial constraints (DCs) are an integrity constraint formalism widely used to detect
inconsistencies in data. Several algorithms have been devised to discover DCs from data …

Intelligent devices often produce time series data that suffer from significant data quality
issues. While the utilization of data dependency in error detection and data repair has been …

The reliability of inferences made by data-driven systems hinges on the data's continued
conformance to the systems' initial settings and assumptions. When serving data (on which …

Functional dependencies (FDs) are widely applied in data management tasks. Since FDs on
data are usually unknown, FD discovery techniques are studied for automatically finding …